Improved algorithm for cleaning high frequency data: An analysis of foreign currency

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Abstract

High-frequency data are notorious for their noise and asynchrony, which may bias or contaminate the empirical analysis of prices and returns. In this study, we develop a novel data filtering approach that simultaneously addresses volatility clustering and irregular spacing, which are inherent characteristics of high-frequency data. Using high frequency currency data collected at five-minute intervals, we find the presence of vast microstructure noise coupled with random volatility clusters, and observe an extremely non-Gaussian distribution of returns. To process non-Gaussian high-frequency data for time series modelling, we propose two efficient and robust standardisation methods that cater for volatility clusters, which clean the data and achieve near-normal distributions. We show that the filtering process efficiently cleans high-frequency data for use in empirical settings while retaining the underlying distributional properties.

Journal Title

Corporate Ownership & Control

Volume

12

Issue

3

DOI

Copyright Statement

Copyright 2015 VirtusInterpress. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.